Changeset 7750 for branches/ProblemInstancesRegressionAndClassification/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective

Timestamp:

04/23/12 16:50:08 (13 years ago)

Author:

sforsten

Message:

#1784:

• merged Problems.DataAnalysis r7273:7748 from trunk
• prepared SymbolicClassificationSingleObjectiveProblem and SymbolicRegressionSingleObjectiveProblem to load and export problem instances

File:

• branches/ProblemInstancesRegressionAndClassification/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveProblem.cs (modified) (4 diffs)

branches/ProblemInstancesRegressionAndClassification/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveProblem.cs

@@ -19 +19 @@
  */
 #endregion
+using System;
+using System.Collections.Generic;
 using System.Linq;
 using HeuristicLab.Common;
@@ -24 +26 @@
 using HeuristicLab.Parameters;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Problems.Instances;
 
 namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {
@@ -29 +32 @@
   [StorableClass]
   [Creatable("Problems")]
-  public class SymbolicClassificationSingleObjectiveProblem : SymbolicDataAnalysisSingleObjectiveProblem<IClassificationProblemData, ISymbolicClassificationSingleObjectiveEvaluator, ISymbolicDataAnalysisSolutionCreator>, IClassificationProblem {
+  public class SymbolicClassificationSingleObjectiveProblem : SymbolicDataAnalysisSingleObjectiveProblem<IClassificationProblemData, ISymbolicClassificationSingleObjectiveEvaluator, ISymbolicDataAnalysisSolutionCreator>, IClassificationProblem,
+    IProblemInstanceConsumer<ClassificationData>, IProblemInstanceExporter<ClassificationData> {
     private const double PunishmentFactor = 10;
     private const int InitialMaximumTreeDepth = 8;
@@ -113 +117 @@
       ProblemData = problemData;
     }
+
+    public void Load(ClassificationData data) {
+      Name = data.Name;
+      Description = data.Description;
+      Dataset dataset = new Dataset(data.InputVariables, data.Values);
+      ProblemData = new ClassificationProblemData(dataset, data.AllowedInputVariables, data.TargetVariable);
+      ProblemData.TrainingPartition.Start = data.TrainingPartitionStart;
+      ProblemData.TrainingPartition.End = data.TrainingPartitionEnd;
+      ProblemData.TestPartition.Start = data.TestPartitionStart;
+      ProblemData.TestPartition.End = data.TestPartitionEnd;
+      OnReset();
+    }
+
+    public ClassificationData Export() {
+      if (!ProblemData.InputVariables.Count.Equals(ProblemData.Dataset.DoubleVariables.Count()))
+        throw new ArgumentException("Not all input variables are double variables! (Export only works with double variables)");
+
+      ClassificationData claData = new ClassificationData();
+      claData.Name = Name;
+      claData.Description = Description;
+      claData.TargetVariable = ProblemData.TargetVariable;
+      claData.InputVariables = ProblemData.InputVariables.Select(x => x.Value).ToArray();
+      claData.AllowedInputVariables = ProblemData.AllowedInputVariables.ToArray();
+      claData.TrainingPartitionStart = ProblemData.TrainingPartition.Start;
+      claData.TrainingPartitionEnd = ProblemData.TrainingPartition.End;
+      claData.TestPartitionStart = ProblemData.TestPartition.Start;
+      claData.TestPartitionEnd = ProblemData.TestPartition.End;
+
+      List<List<double>> data = new List<List<double>>();
+      foreach (var variable in ProblemData.Dataset.DoubleVariables) {
+        data.Add(ProblemData.Dataset.GetDoubleValues(variable).ToList());
+      }
+      claData.Values = Transformer.Transformation(data);
+
+      return claData;
+    }
   }
 }